-------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/kelebogilezvobgo/Desktop/Research/1_ICC-Public-Opinion/ISQ_FINAL/Data/HR-versus-NI_ISQ-Replication_Log-File
> .log
  log type:  text
 opened on:  11 Mar 2019, 20:21:30

. use "HR-versus-NI_ISQ-Replication_Data.dta", clear

. pause on

. 
. 
. 
. ***
. * Summary Statistics
. ***
. 
. *This makes Table 2: Summary Statistics of Participant Characteristics
. tabstat  hr_only ni_only competitive information support_join_court ///
> finance_court icc_previous race_1 gender party_2 party_1 college under_40, stat(mean min max n) col(stat)

    variable |      mean       min       max         N
-------------+----------------------------------------
     hr_only |  .2382353         0         1      1020
     ni_only |  .2647059         0         1      1020
 competitive |  .2431373         0         1      1020
 information |   .522549         0         1      1020
support_jo~t |   .522549         0         1      1020
finance_co~t |  .4892157         0         1      1020
icc_previous |  .5019608         0         1      1020
      race_1 |  .8058824         0         1      1020
      gender |  .5054187         0         1      1015
     party_2 |  .4431373         0         1      1020
     party_1 |  .2401961         0         1      1020
     college |  .5558824         0         1      1020
    under_40 |  .5627451         0         1      1020
------------------------------------------------------

. 
. 
. 
. ***
. * Treatment Effects (Difference in Means)
. ***
. 
. * This makes the main treatment table, Table 3: Approval Rates by Treatment Group for Join Court
. bysort treat_group: su support_join_court /* for columns 2, 3, 5*/

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
support_jo~t |        259    .5598456    .4973667          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
support_jo~t |        243    .6460905    .4791685          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
support_jo~t |        270    .4185185    .4942322          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
support_jo~t |        248    .4758065    .5004243          0          1


. reg support_join_court hr if treat_group==2 | treat_group == 1 /* for table columns 4, 6, 7*/

      Source |       SS           df       MS      Number of obs   =       502
-------------+----------------------------------   F(1, 500)       =      3.91
       Model |  .932545269         1  .932545269   Prob > F        =    0.0487
    Residual |   119.38618       500   .23877236   R-squared       =    0.0078
-------------+----------------------------------   Adj R-squared   =    0.0058
       Total |  120.318725       501  .240157136   Root MSE        =    .48864

------------------------------------------------------------------------------
support_jo~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          hr |    .086245   .0436406     1.98   0.049     .0005034    .1719866
       _cons |   .5598456   .0303628    18.44   0.000     .5001911       .6195
------------------------------------------------------------------------------

. reg support_join_court ni if treat_group==3 | treat_group == 1 /* for table columns 4, 6, 7*/

      Source |       SS           df       MS      Number of obs   =       529
-------------+----------------------------------   F(1, 527)       =     10.74
       Model |   2.6403311         1   2.6403311   Prob > F        =    0.0011
    Residual |  129.529801       527  .245787099   R-squared       =    0.0200
-------------+----------------------------------   Adj R-squared   =    0.0181
       Total |  132.170132       528   .25032222   Root MSE        =    .49577

------------------------------------------------------------------------------
support_jo~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          ni |   -.141327   .0431197    -3.28   0.001    -.2260346   -.0566195
       _cons |   .5598456   .0308056    18.17   0.000     .4993287    .6203624
------------------------------------------------------------------------------

. reg support_join_court competitive if treat_group==4 | treat_group == 1 /* for table columns 4, 6, 7*/

      Source |       SS           df       MS      Number of obs   =       507
-------------+----------------------------------   F(1, 505)       =      3.60
       Model |  .894759578         1  .894759578   Prob > F        =    0.0585
    Residual |  125.677233       505  .248865807   R-squared       =    0.0071
-------------+----------------------------------   Adj R-squared   =    0.0051
       Total |  126.571992       506  .250142277   Root MSE        =    .49886

------------------------------------------------------------------------------
support_jo~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 competitive |  -.0840391   .0443211    -1.90   0.059    -.1711156    .0030374
       _cons |   .5598456   .0309979    18.06   0.000     .4989448    .6207464
------------------------------------------------------------------------------

. 
. 
. 
. ***
. * Figures
. ***
. 
. * This makes Figure 2: Rationale for ICC Support/Opposition, All Respondents, DV = Join Court
. twoway (histogram reason if support_join_court_p==1, percent color(gs10)) ///
>        (histogram reason if support_join_court_p==2, percent ///
>            fcolor(none) lcolor(black)), legend(order(2 "Oppose" 1 "Support" )) ///
>            graphregion(fcolor(white)) 

. 
.            
. * These make Figure 3: Rationale for ICC Support/Opposition by Treatment Group, All Respondents, DV = Join Court
. * Human Rights/Control
. twoway (histogram reason if hr_p==1, percent color(gs10)) ///
>        (histogram reason if hr_p==2, percent ///
>            fcolor(none) lcolor(black)), legend(order(2 "Control" 1 "Human Rights" )) ///
>            graphregion(fcolor(white))

. * National Interest/Control     
. twoway (histogram reason if ni_p==1, percent color(gs10)) ///
>        (histogram reason if ni_p==2, percent ///
>            fcolor(none) lcolor(black)), legend(order(2 "Control" 1 "National Interest" )) ///
>            graphregion(fcolor(white)) 

. * Competitive/Control  
. twoway (histogram reason if competitive_p==1, percent color(gs10)) ///
>        (histogram reason if competitive_p==2, percent ///
>            fcolor(none) lcolor(black)), legend(order(2 "Control" 1 "Competitive" )) ///
>            graphregion(fcolor(white)) 

.            
.            
. * This makes Figure 4a: Join Court by Information
. twoway (histogram join_court if information_p==1, percent color(gs10)) ///
>        (histogram join_court if information_p==2, percent ///
>            fcolor(none) lcolor(black)), legend(order(2 "No Information" 1 "Information" )) ///
>            graphregion(fcolor(white))

. 
. * This makes Figure 4b: Join Court by Prior Knowledge
. twoway (histogram join_court if knowledge_p==1, percent color(gs10)) ///
>        (histogram join_court if knowledge_p==2, percent ///
>            fcolor(none) lcolor(black)), legend(order(2 "No Prior Knowledge" 1 "Prior Knowledge")) ///
>            graphregion(fcolor(white))

. 
. 
.           
. ***
. * Setting Global Controls for Regression Analysis
. ***
. 
. * Treatments
. global treatments hr_only ni_only competitive information

. 
. * Party_ID
. global party party_2 party_1 

. 
. * Demographics
. global demographics race_1 gender $party college under_40

. 
. * IO attitudes
. global io io_use io_go io_eff io_bi

. 
. * UN attitudes
. global un un_use un_go un_eff  un_bi

. 
. * World Affairs Knowledge
. global world_affairs icc_previous reads_news watches_news

. 
. * IO Interactions
. global io_interactions io_use hr_io_use ni_io_use competitive_io_use ///
> io_go hr_io_go ni_io_go competitive_io_go ///
> io_eff hr_io_eff ni_io_eff competitive_io_eff ///
> io_bi hr_io_bi ni_io_bi competitive_io_bi

. 
. * UN Interactions
. global un_interactions un_use hr_un_use ni_un_use competitive_un_use ///
> un_go hr_un_go ni_un_go competitive_un_go ///
> un_eff hr_un_eff ni_un_eff competitive_un_eff ///
> un_bi hr_un_bi ni_un_bi competitive_un_bi

. 
. * World Affairs Interactions
. global wa_interactions hr_icc_previous ni_icc_previous competitive_icc_previous ///
> hr_reads_news  ni_reads_news competitive_reads_news ///
> hr_watches_news ni_watches_news competitive_watches_news

. 
. 
. 
. ***
. * Regression Analysis: Main Models (see Table 4 in main text)
. ***
. 
. eststo clear

. 
. eststo: logit support_join_court $treatments

Iteration 0:   log likelihood = -705.97252  
Iteration 1:   log likelihood = -690.55093  
Iteration 2:   log likelihood = -690.54282  
Iteration 3:   log likelihood = -690.54282  

Logistic regression                             Number of obs     =      1,020
                                                LR chi2(4)        =      30.86
                                                Prob > chi2       =     0.0000
Log likelihood = -690.54282                     Pseudo R2         =     0.0219

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |    .366824   .1837398     2.00   0.046     .0067005    .7269474
           ni_only |  -.5708077   .1758014    -3.25   0.001    -.9153721   -.2262434
       competitive |  -.3368484   .1784601    -1.89   0.059    -.6866239     .012927
       information |   .0788508   .1277104     0.62   0.537    -.1714569    .3291586
             _cons |   .1983095   .1425889     1.39   0.164    -.0811596    .4777786
------------------------------------------------------------------------------------
(est1 stored)

. eststo: logit support_join_court $treatments $demographics

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -632.09072  
Iteration 2:   log likelihood = -632.03392  
Iteration 3:   log likelihood = -632.03392  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(10)       =     141.36
                                                Prob > chi2       =     0.0000
Log likelihood = -632.03392                     Pseudo R2         =     0.1006

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .4884261   .1963479     2.49   0.013     .1035914    .8732609
           ni_only |  -.6761411   .1880815    -3.59   0.000    -1.044774   -.3075082
       competitive |  -.3053003   .1896643    -1.61   0.107    -.6770356    .0664349
       information |   .0406797   .1358217     0.30   0.765     -.225526    .3068854
            race_1 |   .2628548   .1729056     1.52   0.128     -.076034    .6017436
            gender |   .0619356   .1388788     0.45   0.656    -.2102619    .3341331
           party_2 |    1.03899   .1587775     6.54   0.000     .7277922    1.350189
           party_1 |  -.4524145   .1833265    -2.47   0.014    -.8117278   -.0931011
           college |   .0933063   .1374726     0.68   0.497     -.176135    .3627475
          under_40 |   .6222124   .1405068     4.43   0.000     .3468242    .8976007
             _cons |  -.7845877   .2684209    -2.92   0.003    -1.310683   -.2584924
------------------------------------------------------------------------------------
(est2 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -631.05273  
Iteration 2:   log likelihood = -631.00129  
Iteration 3:   log likelihood = -631.00129  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(13)       =     143.43
                                                Prob > chi2       =     0.0000
Log likelihood = -631.00129                     Pseudo R2         =     0.1021

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .4855451   .1964546     2.47   0.013     .1005011    .8705891
           ni_only |  -.6827329   .1887376    -3.62   0.000    -1.052652    -.312814
       competitive |  -.3230234   .1904126    -1.70   0.090    -.6962252    .0501783
       information |   .0406089   .1359657     0.30   0.765     -.225879    .3070969
            race_1 |   .2654422   .1737831     1.53   0.127    -.0751664    .6060508
            gender |   .1047342   .1425481     0.73   0.463    -.1746548    .3841233
           party_2 |   1.039987   .1591751     6.53   0.000     .7280091    1.351964
           party_1 |  -.4497779   .1862396    -2.42   0.016    -.8148008   -.0847551
           college |   .0603727   .1402293     0.43   0.667    -.2144716     .335217
          under_40 |   .6742746   .1470232     4.59   0.000     .3861144    .9624349
      icc_previous |    .167816   .1468767     1.14   0.253    -.1200571    .4556891
        reads_news |   .0212516   .1603951     0.13   0.895    -.2931171    .3356203
      watches_news |    .081259   .1547688     0.53   0.600    -.2220823    .3846004
             _cons |  -.9482504   .2950092    -3.21   0.001    -1.526458    -.370043
------------------------------------------------------------------------------------
(est3 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs $io 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -579.32928  
Iteration 2:   log likelihood = -578.35667  
Iteration 3:   log likelihood = -578.35415  
Iteration 4:   log likelihood = -578.35415  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(17)       =     248.72
                                                Prob > chi2       =     0.0000
Log likelihood = -578.35415                     Pseudo R2         =     0.1770

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .5468868   .2090566     2.62   0.009     .1371434    .9566301
           ni_only |   -.654423   .1988368    -3.29   0.001    -1.044136   -.2647099
       competitive |  -.2975942   .2011585    -1.48   0.139    -.6918576    .0966692
       information |   .0609227   .1439008     0.42   0.672    -.2211177    .3429631
            race_1 |   .2431616   .1831366     1.33   0.184    -.1157796    .6021028
            gender |   .0070534   .1510212     0.05   0.963    -.2889427    .3030494
           party_2 |   .6836897   .1696309     4.03   0.000     .3512193     1.01616
           party_1 |  -.4953281   .2017985    -2.45   0.014    -.8908459   -.0998103
           college |    .125138   .1485122     0.84   0.399    -.1659405    .4162166
          under_40 |   .6515671   .1549104     4.21   0.000     .3479484    .9551859
      icc_previous |   .2949608   .1561467     1.89   0.059    -.0110811    .6010027
        reads_news |   .0001492   .1690293     0.00   0.999    -.3311421    .3314405
      watches_news |   .0072847    .162962     0.04   0.964     -.312115    .3266844
            io_use |   1.120024   .2033031     5.51   0.000     .7215569     1.51849
             io_go |   .4190249   .1696126     2.47   0.013     .0865904    .7514595
            io_eff |   .3755107   .1692943     2.22   0.027     .0436999    .7073215
             io_bi |  -.4385032   .1488079    -2.95   0.003    -.7301612   -.1468451
             _cons |  -1.850919   .3592021    -5.15   0.000    -2.554942   -1.146896
------------------------------------------------------------------------------------
(est4 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs $un 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -588.21954  
Iteration 2:   log likelihood = -587.71449  
Iteration 3:   log likelihood = -587.71391  
Iteration 4:   log likelihood = -587.71391  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(17)       =     230.00
                                                Prob > chi2       =     0.0000
Log likelihood = -587.71391                     Pseudo R2         =     0.1637

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .5681914   .2067009     2.75   0.006     .1630651    .9733176
           ni_only |  -.6652921   .1980389    -3.36   0.001    -1.053441    -.277143
       competitive |  -.3041618   .1994378    -1.53   0.127    -.6950527     .086729
       information |   .0279638   .1422725     0.20   0.844    -.2508852    .3068128
            race_1 |   .2732278   .1821367     1.50   0.134    -.0837536    .6302092
            gender |   -.009501   .1502657    -0.06   0.950    -.3040164    .2850144
           party_2 |   .7239334   .1688776     4.29   0.000     .3929395    1.054927
           party_1 |  -.3960543   .1980712    -2.00   0.046    -.7842668   -.0078418
           college |   .0891864   .1469339     0.61   0.544    -.1987987    .3771715
          under_40 |   .6240305   .1546328     4.04   0.000     .3209558    .9271052
      icc_previous |   .3267615   .1559276     2.10   0.036      .021149    .6323739
        reads_news |   .0352357   .1681536     0.21   0.834    -.2943393    .3648107
      watches_news |   .0524828   .1626912     0.32   0.747    -.2663862    .3713518
            un_use |   .5454788   .1914675     2.85   0.004     .1702093    .9207483
             un_go |   .5962498   .1883871     3.17   0.002     .2270178    .9654818
            un_eff |   .1854957   .1802977     1.03   0.304    -.1678814    .5388728
             un_bi |   -.533995    .150936    -3.54   0.000     -.829824   -.2381659
             _cons |   -1.37879   .3427705    -4.02   0.000    -2.050607   -.7069717
------------------------------------------------------------------------------------
(est5 stored)

. esttab using main.tex, replace label b(3) se(2) nomtitles /*
> */ title("Framing Effects on Court Membership Opinion") star(+ 0.10 * 0.05 ** 0.01 *** 0.001)  
(output written to main.tex)

. 
. 
. eststo A: logit support_join_court $treatments $demographics $world_affairs $io 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -579.32928  
Iteration 2:   log likelihood = -578.35667  
Iteration 3:   log likelihood = -578.35415  
Iteration 4:   log likelihood = -578.35415  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(17)       =     248.72
                                                Prob > chi2       =     0.0000
Log likelihood = -578.35415                     Pseudo R2         =     0.1770

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .5468868   .2090566     2.62   0.009     .1371434    .9566301
           ni_only |   -.654423   .1988368    -3.29   0.001    -1.044136   -.2647099
       competitive |  -.2975942   .2011585    -1.48   0.139    -.6918576    .0966692
       information |   .0609227   .1439008     0.42   0.672    -.2211177    .3429631
            race_1 |   .2431616   .1831366     1.33   0.184    -.1157796    .6021028
            gender |   .0070534   .1510212     0.05   0.963    -.2889427    .3030494
           party_2 |   .6836897   .1696309     4.03   0.000     .3512193     1.01616
           party_1 |  -.4953281   .2017985    -2.45   0.014    -.8908459   -.0998103
           college |    .125138   .1485122     0.84   0.399    -.1659405    .4162166
          under_40 |   .6515671   .1549104     4.21   0.000     .3479484    .9551859
      icc_previous |   .2949608   .1561467     1.89   0.059    -.0110811    .6010027
        reads_news |   .0001492   .1690293     0.00   0.999    -.3311421    .3314405
      watches_news |   .0072847    .162962     0.04   0.964     -.312115    .3266844
            io_use |   1.120024   .2033031     5.51   0.000     .7215569     1.51849
             io_go |   .4190249   .1696126     2.47   0.013     .0865904    .7514595
            io_eff |   .3755107   .1692943     2.22   0.027     .0436999    .7073215
             io_bi |  -.4385032   .1488079    -2.95   0.003    -.7301612   -.1468451
             _cons |  -1.850919   .3592021    -5.15   0.000    -2.554942   -1.146896
------------------------------------------------------------------------------------

. coefplot A, xline(0) xtitle(Coefficients) drop(_cons) graphregion(fcolor(white)) msymbol(d) mfcolor(gs16)

. 
. 
. eststo B: logit support_join_court $treatments $demographics $world_affairs $un 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -588.21954  
Iteration 2:   log likelihood = -587.71449  
Iteration 3:   log likelihood = -587.71391  
Iteration 4:   log likelihood = -587.71391  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(17)       =     230.00
                                                Prob > chi2       =     0.0000
Log likelihood = -587.71391                     Pseudo R2         =     0.1637

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .5681914   .2067009     2.75   0.006     .1630651    .9733176
           ni_only |  -.6652921   .1980389    -3.36   0.001    -1.053441    -.277143
       competitive |  -.3041618   .1994378    -1.53   0.127    -.6950527     .086729
       information |   .0279638   .1422725     0.20   0.844    -.2508852    .3068128
            race_1 |   .2732278   .1821367     1.50   0.134    -.0837536    .6302092
            gender |   -.009501   .1502657    -0.06   0.950    -.3040164    .2850144
           party_2 |   .7239334   .1688776     4.29   0.000     .3929395    1.054927
           party_1 |  -.3960543   .1980712    -2.00   0.046    -.7842668   -.0078418
           college |   .0891864   .1469339     0.61   0.544    -.1987987    .3771715
          under_40 |   .6240305   .1546328     4.04   0.000     .3209558    .9271052
      icc_previous |   .3267615   .1559276     2.10   0.036      .021149    .6323739
        reads_news |   .0352357   .1681536     0.21   0.834    -.2943393    .3648107
      watches_news |   .0524828   .1626912     0.32   0.747    -.2663862    .3713518
            un_use |   .5454788   .1914675     2.85   0.004     .1702093    .9207483
             un_go |   .5962498   .1883871     3.17   0.002     .2270178    .9654818
            un_eff |   .1854957   .1802977     1.03   0.304    -.1678814    .5388728
             un_bi |   -.533995    .150936    -3.54   0.000     -.829824   -.2381659
             _cons |   -1.37879   .3427705    -4.02   0.000    -2.050607   -.7069717
------------------------------------------------------------------------------------

. coefplot B, xline(0) xtitle(Coefficients) drop(_cons) graphregion(fcolor(white)) msymbol(d) mfcolor(gs16)

. 
. 
. 
. ***
. * Interaction Models (see Tables A2-A4 in supplementary appendix)
. ***
. 
. * IO attitudes
. eststo clear

. eststo: logit support_join_court $treatments

Iteration 0:   log likelihood = -705.97252  
Iteration 1:   log likelihood = -690.55093  
Iteration 2:   log likelihood = -690.54282  
Iteration 3:   log likelihood = -690.54282  

Logistic regression                             Number of obs     =      1,020
                                                LR chi2(4)        =      30.86
                                                Prob > chi2       =     0.0000
Log likelihood = -690.54282                     Pseudo R2         =     0.0219

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |    .366824   .1837398     2.00   0.046     .0067005    .7269474
           ni_only |  -.5708077   .1758014    -3.25   0.001    -.9153721   -.2262434
       competitive |  -.3368484   .1784601    -1.89   0.059    -.6866239     .012927
       information |   .0788508   .1277104     0.62   0.537    -.1714569    .3291586
             _cons |   .1983095   .1425889     1.39   0.164    -.0811596    .4777786
------------------------------------------------------------------------------------
(est1 stored)

. eststo: logit support_join_court $treatments $demographics

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -632.09072  
Iteration 2:   log likelihood = -632.03392  
Iteration 3:   log likelihood = -632.03392  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(10)       =     141.36
                                                Prob > chi2       =     0.0000
Log likelihood = -632.03392                     Pseudo R2         =     0.1006

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .4884261   .1963479     2.49   0.013     .1035914    .8732609
           ni_only |  -.6761411   .1880815    -3.59   0.000    -1.044774   -.3075082
       competitive |  -.3053003   .1896643    -1.61   0.107    -.6770356    .0664349
       information |   .0406797   .1358217     0.30   0.765     -.225526    .3068854
            race_1 |   .2628548   .1729056     1.52   0.128     -.076034    .6017436
            gender |   .0619356   .1388788     0.45   0.656    -.2102619    .3341331
           party_2 |    1.03899   .1587775     6.54   0.000     .7277922    1.350189
           party_1 |  -.4524145   .1833265    -2.47   0.014    -.8117278   -.0931011
           college |   .0933063   .1374726     0.68   0.497     -.176135    .3627475
          under_40 |   .6222124   .1405068     4.43   0.000     .3468242    .8976007
             _cons |  -.7845877   .2684209    -2.92   0.003    -1.310683   -.2584924
------------------------------------------------------------------------------------
(est2 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -631.05273  
Iteration 2:   log likelihood = -631.00129  
Iteration 3:   log likelihood = -631.00129  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(13)       =     143.43
                                                Prob > chi2       =     0.0000
Log likelihood = -631.00129                     Pseudo R2         =     0.1021

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .4855451   .1964546     2.47   0.013     .1005011    .8705891
           ni_only |  -.6827329   .1887376    -3.62   0.000    -1.052652    -.312814
       competitive |  -.3230234   .1904126    -1.70   0.090    -.6962252    .0501783
       information |   .0406089   .1359657     0.30   0.765     -.225879    .3070969
            race_1 |   .2654422   .1737831     1.53   0.127    -.0751664    .6060508
            gender |   .1047342   .1425481     0.73   0.463    -.1746548    .3841233
           party_2 |   1.039987   .1591751     6.53   0.000     .7280091    1.351964
           party_1 |  -.4497779   .1862396    -2.42   0.016    -.8148008   -.0847551
           college |   .0603727   .1402293     0.43   0.667    -.2144716     .335217
          under_40 |   .6742746   .1470232     4.59   0.000     .3861144    .9624349
      icc_previous |    .167816   .1468767     1.14   0.253    -.1200571    .4556891
        reads_news |   .0212516   .1603951     0.13   0.895    -.2931171    .3356203
      watches_news |    .081259   .1547688     0.53   0.600    -.2220823    .3846004
             _cons |  -.9482504   .2950092    -3.21   0.001    -1.526458    -.370043
------------------------------------------------------------------------------------
(est3 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs $io 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -579.32928  
Iteration 2:   log likelihood = -578.35667  
Iteration 3:   log likelihood = -578.35415  
Iteration 4:   log likelihood = -578.35415  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(17)       =     248.72
                                                Prob > chi2       =     0.0000
Log likelihood = -578.35415                     Pseudo R2         =     0.1770

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .5468868   .2090566     2.62   0.009     .1371434    .9566301
           ni_only |   -.654423   .1988368    -3.29   0.001    -1.044136   -.2647099
       competitive |  -.2975942   .2011585    -1.48   0.139    -.6918576    .0966692
       information |   .0609227   .1439008     0.42   0.672    -.2211177    .3429631
            race_1 |   .2431616   .1831366     1.33   0.184    -.1157796    .6021028
            gender |   .0070534   .1510212     0.05   0.963    -.2889427    .3030494
           party_2 |   .6836897   .1696309     4.03   0.000     .3512193     1.01616
           party_1 |  -.4953281   .2017985    -2.45   0.014    -.8908459   -.0998103
           college |    .125138   .1485122     0.84   0.399    -.1659405    .4162166
          under_40 |   .6515671   .1549104     4.21   0.000     .3479484    .9551859
      icc_previous |   .2949608   .1561467     1.89   0.059    -.0110811    .6010027
        reads_news |   .0001492   .1690293     0.00   0.999    -.3311421    .3314405
      watches_news |   .0072847    .162962     0.04   0.964     -.312115    .3266844
            io_use |   1.120024   .2033031     5.51   0.000     .7215569     1.51849
             io_go |   .4190249   .1696126     2.47   0.013     .0865904    .7514595
            io_eff |   .3755107   .1692943     2.22   0.027     .0436999    .7073215
             io_bi |  -.4385032   .1488079    -2.95   0.003    -.7301612   -.1468451
             _cons |  -1.850919   .3592021    -5.15   0.000    -2.554942   -1.146896
------------------------------------------------------------------------------------
(est4 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs $io_interactions 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -568.64817  
Iteration 2:   log likelihood = -567.45092  
Iteration 3:   log likelihood =  -567.4484  
Iteration 4:   log likelihood =  -567.4484  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(29)       =     270.54
                                                Prob > chi2       =     0.0000
Log likelihood =  -567.4484                     Pseudo R2         =     0.1925

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   1.134516   .5831983     1.95   0.052    -.0085318    2.277564
           ni_only |  -.0527616   .6076559    -0.09   0.931    -1.243745    1.138222
       competitive |   .3802011     .61337     0.62   0.535    -.8219819    1.582384
       information |   .0536105   .1467599     0.37   0.715    -.2340337    .3412547
            race_1 |   .2654348   .1864922     1.42   0.155    -.1000832    .6309529
            gender |   .0464686   .1536501     0.30   0.762    -.2546801    .3476174
           party_2 |   .6832953   .1725865     3.96   0.000     .3450321    1.021559
           party_1 |  -.5163463   .2052485    -2.52   0.012    -.9186259   -.1140667
           college |   .0973128   .1508266     0.65   0.519    -.1983019    .3929275
          under_40 |   .7045814   .1580904     4.46   0.000     .3947299    1.014433
      icc_previous |   .3280145   .1585241     2.07   0.039      .017313     .638716
        reads_news |   .0313778   .1729187     0.18   0.856    -.3075366    .3702922
      watches_news |    .003652   .1651892     0.02   0.982     -.320113     .327417
            io_use |   1.610189   .4276536     3.77   0.000     .7720031    2.448374
         hr_io_use |  -.8145027    .574816    -1.42   0.156    -1.941121     .312116
         ni_io_use |  -.8023748   .5986436    -1.34   0.180    -1.975695    .3709452
competitive_io_use |  -.3413641   .6046113    -0.56   0.572     -1.52638    .8436522
             io_go |    .206433   .3425878     0.60   0.547    -.4650266    .8778927
          hr_io_go |   .5468886   .4875303     1.12   0.262    -.4086532     1.50243
          ni_io_go |   .7569797   .4812244     1.57   0.116    -.1862029    1.700162
 competitive_io_go |  -.3618788   .4889508    -0.74   0.459    -1.320205    .5964473
            io_eff |    .071512   .3314235     0.22   0.829     -.578066      .72109
         hr_io_eff |   .3036873   .5065122     0.60   0.549    -.6890583    1.296433
         ni_io_eff |   .1131102   .4655333     0.24   0.808    -.7993182    1.025539
competitive_io_eff |   .9228325   .4747185     1.94   0.052    -.0075986    1.853264
             io_bi |    .195836   .2978571     0.66   0.511    -.3879532    .7796252
          hr_io_bi |  -.6881963   .4424635    -1.56   0.120    -1.555409    .1790162
          ni_io_bi |  -.7785336   .4117515    -1.89   0.059    -1.585552    .0284846
 competitive_io_bi |  -1.143256    .424836    -2.69   0.007    -1.975919   -.3105923
             _cons |  -2.415607   .5265689    -4.59   0.000    -3.447663   -1.383551
------------------------------------------------------------------------------------
(est5 stored)

. esttab using io_interactions.tex, replace label se nomtitles /*
> */ title("Interaction of Treatments and IO Attitudes") star(+ 0.10 * 0.05 ** 0.01 *** 0.001)  
(output written to io_interactions.tex)

. 
. * UN attitudes
. eststo clear

. eststo: logit support_join_court $treatments

Iteration 0:   log likelihood = -705.97252  
Iteration 1:   log likelihood = -690.55093  
Iteration 2:   log likelihood = -690.54282  
Iteration 3:   log likelihood = -690.54282  

Logistic regression                             Number of obs     =      1,020
                                                LR chi2(4)        =      30.86
                                                Prob > chi2       =     0.0000
Log likelihood = -690.54282                     Pseudo R2         =     0.0219

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |    .366824   .1837398     2.00   0.046     .0067005    .7269474
           ni_only |  -.5708077   .1758014    -3.25   0.001    -.9153721   -.2262434
       competitive |  -.3368484   .1784601    -1.89   0.059    -.6866239     .012927
       information |   .0788508   .1277104     0.62   0.537    -.1714569    .3291586
             _cons |   .1983095   .1425889     1.39   0.164    -.0811596    .4777786
------------------------------------------------------------------------------------
(est1 stored)

. eststo: logit support_join_court $treatments $demographics

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -632.09072  
Iteration 2:   log likelihood = -632.03392  
Iteration 3:   log likelihood = -632.03392  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(10)       =     141.36
                                                Prob > chi2       =     0.0000
Log likelihood = -632.03392                     Pseudo R2         =     0.1006

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .4884261   .1963479     2.49   0.013     .1035914    .8732609
           ni_only |  -.6761411   .1880815    -3.59   0.000    -1.044774   -.3075082
       competitive |  -.3053003   .1896643    -1.61   0.107    -.6770356    .0664349
       information |   .0406797   .1358217     0.30   0.765     -.225526    .3068854
            race_1 |   .2628548   .1729056     1.52   0.128     -.076034    .6017436
            gender |   .0619356   .1388788     0.45   0.656    -.2102619    .3341331
           party_2 |    1.03899   .1587775     6.54   0.000     .7277922    1.350189
           party_1 |  -.4524145   .1833265    -2.47   0.014    -.8117278   -.0931011
           college |   .0933063   .1374726     0.68   0.497     -.176135    .3627475
          under_40 |   .6222124   .1405068     4.43   0.000     .3468242    .8976007
             _cons |  -.7845877   .2684209    -2.92   0.003    -1.310683   -.2584924
------------------------------------------------------------------------------------
(est2 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -631.05273  
Iteration 2:   log likelihood = -631.00129  
Iteration 3:   log likelihood = -631.00129  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(13)       =     143.43
                                                Prob > chi2       =     0.0000
Log likelihood = -631.00129                     Pseudo R2         =     0.1021

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .4855451   .1964546     2.47   0.013     .1005011    .8705891
           ni_only |  -.6827329   .1887376    -3.62   0.000    -1.052652    -.312814
       competitive |  -.3230234   .1904126    -1.70   0.090    -.6962252    .0501783
       information |   .0406089   .1359657     0.30   0.765     -.225879    .3070969
            race_1 |   .2654422   .1737831     1.53   0.127    -.0751664    .6060508
            gender |   .1047342   .1425481     0.73   0.463    -.1746548    .3841233
           party_2 |   1.039987   .1591751     6.53   0.000     .7280091    1.351964
           party_1 |  -.4497779   .1862396    -2.42   0.016    -.8148008   -.0847551
           college |   .0603727   .1402293     0.43   0.667    -.2144716     .335217
          under_40 |   .6742746   .1470232     4.59   0.000     .3861144    .9624349
      icc_previous |    .167816   .1468767     1.14   0.253    -.1200571    .4556891
        reads_news |   .0212516   .1603951     0.13   0.895    -.2931171    .3356203
      watches_news |    .081259   .1547688     0.53   0.600    -.2220823    .3846004
             _cons |  -.9482504   .2950092    -3.21   0.001    -1.526458    -.370043
------------------------------------------------------------------------------------
(est3 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs $un 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -588.21954  
Iteration 2:   log likelihood = -587.71449  
Iteration 3:   log likelihood = -587.71391  
Iteration 4:   log likelihood = -587.71391  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(17)       =     230.00
                                                Prob > chi2       =     0.0000
Log likelihood = -587.71391                     Pseudo R2         =     0.1637

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .5681914   .2067009     2.75   0.006     .1630651    .9733176
           ni_only |  -.6652921   .1980389    -3.36   0.001    -1.053441    -.277143
       competitive |  -.3041618   .1994378    -1.53   0.127    -.6950527     .086729
       information |   .0279638   .1422725     0.20   0.844    -.2508852    .3068128
            race_1 |   .2732278   .1821367     1.50   0.134    -.0837536    .6302092
            gender |   -.009501   .1502657    -0.06   0.950    -.3040164    .2850144
           party_2 |   .7239334   .1688776     4.29   0.000     .3929395    1.054927
           party_1 |  -.3960543   .1980712    -2.00   0.046    -.7842668   -.0078418
           college |   .0891864   .1469339     0.61   0.544    -.1987987    .3771715
          under_40 |   .6240305   .1546328     4.04   0.000     .3209558    .9271052
      icc_previous |   .3267615   .1559276     2.10   0.036      .021149    .6323739
        reads_news |   .0352357   .1681536     0.21   0.834    -.2943393    .3648107
      watches_news |   .0524828   .1626912     0.32   0.747    -.2663862    .3713518
            un_use |   .5454788   .1914675     2.85   0.004     .1702093    .9207483
             un_go |   .5962498   .1883871     3.17   0.002     .2270178    .9654818
            un_eff |   .1854957   .1802977     1.03   0.304    -.1678814    .5388728
             un_bi |   -.533995    .150936    -3.54   0.000     -.829824   -.2381659
             _cons |   -1.37879   .3427705    -4.02   0.000    -2.050607   -.7069717
------------------------------------------------------------------------------------
(est4 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs $un_interactions

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -580.24716  
Iteration 2:   log likelihood = -579.36969  
Iteration 3:   log likelihood = -579.36693  
Iteration 4:   log likelihood = -579.36693  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(29)       =     246.70
                                                Prob > chi2       =     0.0000
Log likelihood = -579.36693                     Pseudo R2         =     0.1755

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .6324515   .5318115     1.19   0.234    -.4098799    1.674783
           ni_only |  -.6329871   .4862679    -1.30   0.193    -1.586055    .3200804
       competitive |  -.0184718   .4978263    -0.04   0.970    -.9941934    .9572499
       information |   .0058701   .1450727     0.04   0.968    -.2784671    .2902074
            race_1 |   .2156387   .1844057     1.17   0.242    -.1457897    .5770672
            gender |  -.0223105    .152307    -0.15   0.884    -.3208266    .2762057
           party_2 |   .7286827    .171021     4.26   0.000     .3934878    1.063878
           party_1 |  -.4054621   .2008509    -2.02   0.044    -.7991226   -.0118016
           college |   .0635456   .1489838     0.43   0.670    -.2284573    .3555485
          under_40 |   .6082181   .1564345     3.89   0.000     .3016122    .9148241
      icc_previous |   .3032017   .1578406     1.92   0.055    -.0061602    .6125636
        reads_news |   .0451453   .1703912     0.26   0.791    -.2888152    .3791058
      watches_news |   .0869999   .1646915     0.53   0.597    -.2357894    .4097892
            un_use |     .79268   .4025968     1.97   0.049     .0036048    1.581755
         hr_un_use |  -.6171601   .5494908    -1.12   0.261    -1.694142    .4598221
         ni_un_use |  -.0584208   .5496318    -0.11   0.915    -1.135679    1.018838
competitive_un_use |  -.2581426   .5556359    -0.46   0.642    -1.347169    .8308837
             un_go |   1.179123   .4025601     2.93   0.003     .3901193    1.968126
          hr_un_go |  -.6947518   .5422896    -1.28   0.200     -1.75762    .3681162
          ni_un_go |  -.6232247   .5456847    -1.14   0.253    -1.692747    .4462977
 competitive_un_go |  -.7802066    .560411    -1.39   0.164    -1.878592    .3181788
            un_eff |  -.8278664   .4004435    -2.07   0.039    -1.612721   -.0430115
         hr_un_eff |   1.788113   .5499688     3.25   0.001     .7101936    2.866032
         ni_un_eff |   .9102523   .5268121     1.73   0.084    -.1222804    1.942785
competitive_un_eff |   1.312459   .5401435     2.43   0.015     .2537968    2.371121
             un_bi |  -.2773364   .3059287    -0.91   0.365    -.8769456    .3222728
          hr_un_bi |  -.1220833   .4582191    -0.27   0.790    -1.020176    .7760095
          ni_un_bi |  -.1989262   .4146298    -0.48   0.631    -1.011586    .6137332
 competitive_un_bi |  -.6609844    .429006    -1.54   0.123    -1.501821    .1798518
             _cons |  -1.423495   .4515447    -3.15   0.002    -2.308506   -.5384836
------------------------------------------------------------------------------------
(est5 stored)

. esttab using un_interactions.tex, replace label se nomtitles /*
> */ title("Interaction of Treatments and UN Attitudes") star(+ 0.10 * 0.05 ** 0.01 *** 0.001)  
(output written to un_interactions.tex)

. 
. * World Affairs Knowledge
. eststo clear

. eststo: logit support_join_court $treatments

Iteration 0:   log likelihood = -705.97252  
Iteration 1:   log likelihood = -690.55093  
Iteration 2:   log likelihood = -690.54282  
Iteration 3:   log likelihood = -690.54282  

Logistic regression                             Number of obs     =      1,020
                                                LR chi2(4)        =      30.86
                                                Prob > chi2       =     0.0000
Log likelihood = -690.54282                     Pseudo R2         =     0.0219

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |    .366824   .1837398     2.00   0.046     .0067005    .7269474
           ni_only |  -.5708077   .1758014    -3.25   0.001    -.9153721   -.2262434
       competitive |  -.3368484   .1784601    -1.89   0.059    -.6866239     .012927
       information |   .0788508   .1277104     0.62   0.537    -.1714569    .3291586
             _cons |   .1983095   .1425889     1.39   0.164    -.0811596    .4777786
------------------------------------------------------------------------------------
(est1 stored)

. eststo: logit support_join_court $treatments $demographics

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -632.09072  
Iteration 2:   log likelihood = -632.03392  
Iteration 3:   log likelihood = -632.03392  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(10)       =     141.36
                                                Prob > chi2       =     0.0000
Log likelihood = -632.03392                     Pseudo R2         =     0.1006

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .4884261   .1963479     2.49   0.013     .1035914    .8732609
           ni_only |  -.6761411   .1880815    -3.59   0.000    -1.044774   -.3075082
       competitive |  -.3053003   .1896643    -1.61   0.107    -.6770356    .0664349
       information |   .0406797   .1358217     0.30   0.765     -.225526    .3068854
            race_1 |   .2628548   .1729056     1.52   0.128     -.076034    .6017436
            gender |   .0619356   .1388788     0.45   0.656    -.2102619    .3341331
           party_2 |    1.03899   .1587775     6.54   0.000     .7277922    1.350189
           party_1 |  -.4524145   .1833265    -2.47   0.014    -.8117278   -.0931011
           college |   .0933063   .1374726     0.68   0.497     -.176135    .3627475
          under_40 |   .6222124   .1405068     4.43   0.000     .3468242    .8976007
             _cons |  -.7845877   .2684209    -2.92   0.003    -1.310683   -.2584924
------------------------------------------------------------------------------------
(est2 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -631.05273  
Iteration 2:   log likelihood = -631.00129  
Iteration 3:   log likelihood = -631.00129  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(13)       =     143.43
                                                Prob > chi2       =     0.0000
Log likelihood = -631.00129                     Pseudo R2         =     0.1021

------------------------------------------------------------------------------------
support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           hr_only |   .4855451   .1964546     2.47   0.013     .1005011    .8705891
           ni_only |  -.6827329   .1887376    -3.62   0.000    -1.052652    -.312814
       competitive |  -.3230234   .1904126    -1.70   0.090    -.6962252    .0501783
       information |   .0406089   .1359657     0.30   0.765     -.225879    .3070969
            race_1 |   .2654422   .1737831     1.53   0.127    -.0751664    .6060508
            gender |   .1047342   .1425481     0.73   0.463    -.1746548    .3841233
           party_2 |   1.039987   .1591751     6.53   0.000     .7280091    1.351964
           party_1 |  -.4497779   .1862396    -2.42   0.016    -.8148008   -.0847551
           college |   .0603727   .1402293     0.43   0.667    -.2144716     .335217
          under_40 |   .6742746   .1470232     4.59   0.000     .3861144    .9624349
      icc_previous |    .167816   .1468767     1.14   0.253    -.1200571    .4556891
        reads_news |   .0212516   .1603951     0.13   0.895    -.2931171    .3356203
      watches_news |    .081259   .1547688     0.53   0.600    -.2220823    .3846004
             _cons |  -.9482504   .2950092    -3.21   0.001    -1.526458    -.370043
------------------------------------------------------------------------------------
(est3 stored)

. eststo: logit support_join_court $treatments $demographics $world_affairs $wa_interactions 

Iteration 0:   log likelihood = -702.71608  
Iteration 1:   log likelihood = -622.08758  
Iteration 2:   log likelihood =   -622.024  
Iteration 3:   log likelihood = -622.02399  

Logistic regression                             Number of obs     =      1,015
                                                LR chi2(22)       =     161.38
                                                Prob > chi2       =     0.0000
Log likelihood = -622.02399                     Pseudo R2         =     0.1148

------------------------------------------------------------------------------------------
      support_join_court |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                 hr_only |   1.047421    .342856     3.05   0.002     .3754358    1.719407
                 ni_only |  -.4845835   .3276588    -1.48   0.139    -1.126783     .157616
             competitive |    .367122   .3329987     1.10   0.270    -.2855435    1.019788
             information |   .0424005   .1378269     0.31   0.758    -.2277353    .3125362
                  race_1 |   .2472453   .1766723     1.40   0.162     -.099026    .5935166
                  gender |   .1051551   .1441919     0.73   0.466    -.1774559    .3877662
                 party_2 |   1.038852   .1612226     6.44   0.000     .7228616    1.354842
                 party_1 |  -.4783486   .1900089    -2.52   0.012    -.8507591    -.105938
                 college |   .0413223   .1421344     0.29   0.771     -.237256    .3199005
                under_40 |   .6651791   .1486975     4.47   0.000     .3737374    .9566209
            icc_previous |   .3294147   .2822051     1.17   0.243    -.2236971    .8825264
              reads_news |   .0161132   .3056263     0.05   0.958    -.5829033    .6151297
            watches_news |    .732692   .2993759     2.45   0.014      .145926    1.319458
         hr_icc_previous |  -.2983253   .4097124    -0.73   0.467    -1.101347    .5046962
         ni_icc_previous |  -.1302228   .3960316    -0.33   0.742    -.9064305    .6459849
competitive_icc_previous |  -.2019787   .4104969    -0.49   0.623    -1.006538    .6025805
           hr_reads_news |  -.3815607   .4448458    -0.86   0.391    -1.253443    .4903211
           ni_reads_news |   .2715638   .4397575     0.62   0.537     -.590345    1.133473
  competitive_reads_news |   .2120907   .4553241     0.47   0.641    -.6803281    1.104509
         hr_watches_news |  -.4453254   .4286995    -1.04   0.299    -1.285561    .3949102
         ni_watches_news |  -.6867206   .4199426    -1.64   0.102    -1.509793    .1363518
competitive_watches_news |  -1.530915   .4368766    -3.50   0.000    -2.387177   -.6746523
                   _cons |  -1.274388   .3397588    -3.75   0.000    -1.940303   -.6084731
------------------------------------------------------------------------------------------
(est4 stored)

. esttab using wa_interactions.tex, replace label se nomtitles /*
> */ title("Interaction of Treatments and World Affairs Knowledge") star(+ 0.10 * 0.05 ** 0.01 *** 0.001)  
(output written to wa_interactions.tex)

. 
. 
. 
. ***
. * Secondary Assessment, DV = Finance Court
. ***
. 
. * This makes Table A1 Approval Rates by Treatment Group for Finance Court
. bysort treat_group: su finance_court 

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
finance_co~t |        259    .4478764    .4982385          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
finance_co~t |        243    .5720165    .4958077          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
finance_co~t |        270          .4    .4908077          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
finance_co~t |        248    .5483871    .4986596          0          1


. reg finance_court hr if treat_group==2 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =       502
-------------+----------------------------------   F(1, 500)       =      7.82
       Model |  1.93208351         1  1.93208351   Prob > F        =    0.0054
    Residual |  123.536044       500  .247072088   R-squared       =    0.0154
-------------+----------------------------------   Adj R-squared   =    0.0134
       Total |  125.468127       501  .250435384   Root MSE        =    .49706

------------------------------------------------------------------------------
finance_co~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          hr |     .12414   .0443926     2.80   0.005      .036921    .2113591
       _cons |   .4478764    .030886    14.50   0.000     .3871941    .5085588
------------------------------------------------------------------------------

. reg finance_court ni if treat_group==3 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =       529
-------------+----------------------------------   F(1, 527)       =      1.24
       Model |  .303006328         1  .303006328   Prob > F        =    0.2661
    Residual |  128.846332       527  .244490194   R-squared       =    0.0023
-------------+----------------------------------   Adj R-squared   =    0.0005
       Total |  129.149338       528   .24460102   Root MSE        =    .49446

------------------------------------------------------------------------------
finance_co~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          ni |  -.0478764   .0430058    -1.11   0.266    -.1323602    .0366074
       _cons |   .4478764   .0307242    14.58   0.000     .3875195    .5082334
------------------------------------------------------------------------------

. reg finance_court competitive if treat_group==4 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =       507
-------------+----------------------------------   F(1, 505)       =      5.15
       Model |  1.27987525         1  1.27987525   Prob > F        =    0.0236
    Residual |  125.465687       505  .248446905   R-squared       =    0.0101
-------------+----------------------------------   Adj R-squared   =    0.0081
       Total |  126.745562       506  .250485301   Root MSE        =    .49844

------------------------------------------------------------------------------
finance_co~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 competitive |   .1005106   .0442838     2.27   0.024     .0135074    .1875139
       _cons |   .4478764   .0309718    14.46   0.000     .3870269     .508726
------------------------------------------------------------------------------

. 
. 
. 
. ***
. * Attitudinal DVs (see supplementary appendix)
. ***
. 
. * Call Senator (Table A5)
. bysort treat_group: su call 

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        call |        259    .1698842    .3762581          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        call |        243    .2427984    .4296592          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        call |        270    .2074074    .4062028          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        call |        248    .2177419    .4135456          0          1


. reg call hr if treat_group==2 | treat_group == 1 

      Source |       SS           df       MS      Number of obs   =       502
-------------+----------------------------------   F(1, 500)       =      4.10
       Model |   .66654022         1   .66654022   Prob > F        =    0.0433
    Residual |  81.1999936       500  .162399987   R-squared       =    0.0081
-------------+----------------------------------   Adj R-squared   =    0.0062
       Total |  81.8665339       501  .163406255   Root MSE        =    .40299

------------------------------------------------------------------------------
        call |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          hr |   .0729142   .0359908     2.03   0.043     .0022023    .1436261
       _cons |   .1698842   .0250405     6.78   0.000     .1206866    .2190818
------------------------------------------------------------------------------

. reg call ni if treat_group==3 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =       529
-------------+----------------------------------   F(1, 527)       =      1.21
       Model |  .186126607         1  .186126607   Prob > F        =    0.2714
    Residual |  80.9102817       527  .153529946   R-squared       =    0.0023
-------------+----------------------------------   Adj R-squared   =    0.0004
       Total |  81.0964083       528  .153591682   Root MSE        =    .39183

------------------------------------------------------------------------------
        call |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          ni |   .0375232   .0340795     1.10   0.271     -.029425    .1044715
       _cons |   .1698842   .0243471     6.98   0.000     .1220549    .2177134
------------------------------------------------------------------------------

. reg call competitive if treat_group==4 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =       507
-------------+----------------------------------   F(1, 505)       =      1.86
       Model |  .290167202         1  .290167202   Prob > F        =    0.1732
    Residual |   78.767032       505  .155974321   R-squared       =    0.0037
-------------+----------------------------------   Adj R-squared   =    0.0017
       Total |  79.0571992       506  .156239524   Root MSE        =    .39494

------------------------------------------------------------------------------
        call |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 competitive |   .0478578   .0350877     1.36   0.173    -.0210781    .1167936
       _cons |   .1698842   .0245401     6.92   0.000     .1216709    .2180975
------------------------------------------------------------------------------

. 
. 
. 
. ***
. * Behavioral DV: Call Senator (see supplementary appendix)
. ***
. 
. * Donate (Table A6)
. bysort treat_group: su donate 

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      donate |        259    .2702703    .4449592          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      donate |        243    .3415638    .4752129          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      donate |        270     .337037    .4735755          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      donate |        248    .2701613    .4449409          0          1


. reg donate hr if treat_group==2 | treat_group == 1 

      Source |       SS           df       MS      Number of obs   =       502
-------------+----------------------------------   F(1, 500)       =      3.01
       Model |  .637239054         1  .637239054   Prob > F        =    0.0832
    Residual |  105.731287       500  .211462574   R-squared       =    0.0060
-------------+----------------------------------   Adj R-squared   =    0.0040
       Total |  106.368526       501  .212312427   Root MSE        =    .45985

------------------------------------------------------------------------------
      donate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          hr |   .0712935   .0410691     1.74   0.083    -.0093958    .1519829
       _cons |   .2702703   .0285737     9.46   0.000     .2141309    .3264096
------------------------------------------------------------------------------

. reg donate ni if treat_group==3 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =       529
-------------+----------------------------------   F(1, 527)       =      2.79
       Model |  .589289289         1  .589289289   Prob > F        =    0.0956
    Residual |  111.410711       527  .211405523   R-squared       =    0.0053
-------------+----------------------------------   Adj R-squared   =    0.0034
       Total |         112       528  .212121212   Root MSE        =    .45979

------------------------------------------------------------------------------
      donate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          ni |   .0667668   .0399903     1.67   0.096    -.0117931    .1453267
       _cons |   .2702703   .0285699     9.46   0.000     .2141455    .3263951
------------------------------------------------------------------------------

. reg donate competitive if treat_group==4 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =       507
-------------+----------------------------------   F(1, 505)       =      0.00
       Model |  1.5047e-06         1  1.5047e-06   Prob > F        =    0.9978
    Residual |  99.9802746       505  .197980742   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0020
       Total |  99.9802761       506  .197589479   Root MSE        =    .44495

------------------------------------------------------------------------------
      donate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 competitive |   -.000109   .0395312    -0.00   0.998    -.0777748    .0775568
       _cons |   .2702703   .0276479     9.78   0.000     .2159513    .3245893
------------------------------------------------------------------------------

. 
. * Write Senator (Table A7)
. bysort treat_group: su write_senator 

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
write_sena~r |        259    .0965251    .2958818          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
write_sena~r |        243    .0987654    .2989626          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
write_sena~r |        270    .0814815    .2740811          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
write_sena~r |        248    .0967742    .2962479          0          1


. reg write_senator hr if treat_group==2 | treat_group == 1 

      Source |       SS           df       MS      Number of obs   =       502
-------------+----------------------------------   F(1, 500)       =      0.01
       Model |  .000629258         1  .000629258   Prob > F        =    0.9328
    Residual |  44.2165022       500  .088433004   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0020
       Total |  44.2171315       501  .088257747   Root MSE        =    .29738

------------------------------------------------------------------------------
write_sena~r |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          hr |   .0022403   .0265587     0.08   0.933      -.04994    .0544206
       _cons |   .0965251   .0184781     5.22   0.000     .0602208    .1328294
------------------------------------------------------------------------------

. reg write_senator ni if treat_group==3 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =       529
-------------+----------------------------------   F(1, 527)       =      0.37
       Model |  .029916603         1  .029916603   Prob > F        =    0.5441
    Residual |    42.79428       527  .081203567   R-squared       =    0.0007
-------------+----------------------------------   Adj R-squared   =   -0.0012
       Total |  42.8241966       528  .081106433   Root MSE        =    .28496

------------------------------------------------------------------------------
write_sena~r |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          ni |  -.0150436   .0247847    -0.61   0.544    -.0637325    .0336453
       _cons |   .0965251   .0177067     5.45   0.000     .0617407    .1313095
------------------------------------------------------------------------------

. reg write_senator competitive if treat_group==4 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =       507
-------------+----------------------------------   F(1, 505)       =      0.00
       Model |  7.8611e-06         1  7.8611e-06   Prob > F        =    0.9924
    Residual |  44.2642919       505  .087652063   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0020
       Total |  44.2642998       506  .087478853   Root MSE        =    .29606

------------------------------------------------------------------------------
write_sena~r |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 competitive |   .0002491   .0263032     0.01   0.992    -.0514282    .0519264
       _cons |   .0965251   .0183963     5.25   0.000     .0603823    .1326679
------------------------------------------------------------------------------

. 
. * Letter Type (Table A8)
. bysort treat_group: su letter_support 

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
letter_sup~t |         25         .92    .2768875          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
letter_sup~t |         23     .826087    .3875534          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 3

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
letter_sup~t |         21    .7619048    .4364358          0          1

-------------------------------------------------------------------------------------------------------------------------------
-> treat_group = 4

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
letter_sup~t |         24    .7916667    .4148511          0          1


. reg letter_support hr if treat_group==2 | treat_group == 1 

      Source |       SS           df       MS      Number of obs   =        48
-------------+----------------------------------   F(1, 46)        =      0.94
       Model |  .105652174         1  .105652174   Prob > F        =    0.3361
    Residual |  5.14434783        46  .111833648   R-squared       =    0.0201
-------------+----------------------------------   Adj R-squared   =   -0.0012
       Total |        5.25        47  .111702128   Root MSE        =    .33442

------------------------------------------------------------------------------
letter_sup~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          hr |   -.093913   .0966213    -0.97   0.336    -.2884017    .1005756
       _cons |        .92   .0668831    13.76   0.000     .7853713    1.054629
------------------------------------------------------------------------------

. reg letter_support ni if treat_group==3 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =        46
-------------+----------------------------------   F(1, 44)        =      2.22
       Model |  .285258799         1  .285258799   Prob > F        =    0.1432
    Residual |  5.64952381        44  .128398268   R-squared       =    0.0481
-------------+----------------------------------   Adj R-squared   =    0.0264
       Total |  5.93478261        45  .131884058   Root MSE        =    .35833

------------------------------------------------------------------------------
letter_sup~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          ni |  -.1580952   .1060666    -1.49   0.143    -.3718585     .055668
       _cons |        .92   .0716654    12.84   0.000     .7755679    1.064432
------------------------------------------------------------------------------

. reg letter_support competitive if treat_group==4 | treat_group == 1

      Source |       SS           df       MS      Number of obs   =        49
-------------+----------------------------------   F(1, 47)        =      1.63
       Model |  .201666667         1  .201666667   Prob > F        =    0.2073
    Residual |  5.79833333        47  .123368794   R-squared       =    0.0336
-------------+----------------------------------   Adj R-squared   =    0.0130
       Total |           6        48        .125   Root MSE        =    .35124

------------------------------------------------------------------------------
letter_sup~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 competitive |  -.1283333   .1003749    -1.28   0.207    -.3302616    .0735949
       _cons |        .92   .0702478    13.10   0.000     .7786797     1.06132
------------------------------------------------------------------------------

. 
end of do-file
